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2024 ASEE Annual Conference and Exposition

作者： Raviv, Daniel Yepes, Juan D. Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

In today's educational landscape, students increasingly seek to grasp basic understanding of mathematical concepts. It is imperative that teaching approaches evolve to meet this demand, especially in the realm of mathematics where there is a gap between understanding the theory and applying its relevant techniques. This paper focuses on addressing the challenges students face when learning calculus, particularly the L'Hopital's Rule. While students may learn how to apply it, many lack a fundamental understanding of its origins, missing the crucial"why" behind its application. The primary objective of this paper is to deepen students' understanding of L'Hopital's Rule by incorporating visualization and intuition techniques with mathematical approaches. We explore the background of L'Hopital's Rule and present three different ways to explain the essence of the rule by: a) "zooming-in" on the intersection of two functions, b) employing the Taylor Series, and c) utilizing Infinitesimal Calculus. By using these approaches, it becomes easier to grasp the essence of L'Hopital's Rule, which is about comparing how fast two functions change as they approach a particular point. Moreover, to provide a comprehensive perspective we share some practical textbook examples that highlight the applications of L'Hopital's Rule. This is followed by a real-life engineering problem that utilizes L'Hopital's Rule. To assess the effectiveness of the new approach for learning L'Hopital's Rule we conducted an in-class anonymous questionnaire. 58 students responded. The results clearly show that understanding the concept of L'Hopital's Rule is either important or very important to students. Most of them praised the visualization and intuition approach for teaching the rule. Even though we did not used activities and exercises, students felt that more hands-on activities and in-class exercises could be very helpful as well. In general, they liked traditional presentations, but were not as excit

关键词： Students

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Trust-based Intelligent Scheme for Mitigating Black Hole Attacks in IoT

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Journal of Harbin Institute of Technology(New Series) 2024年第3期31卷 39-50页

作者： Feroz Khan A.B Weslin D Dilli Babu M Mohammed Muzaffar Hussain Department of Computer Science Syed Hameedha Arts and Science CollegeKilakarai 623806Tamil NaduIndia Department of Information Technology Mohamed Sathak AJ College of EngineeringSiruseriChennai 603103India Department of Information Technology Panimalar Engineering CollegeChennai 600123India Department of Information Technology C.Abdul Hakeem College of Engineering and TechnologyMelvisharam 632509India

Internet of Things(IoT)networks are characterized by a multitude of wireless,interconnected devices that can dynamically join or exit the network without centralized administration or fixed infrastructure for *** multipath routing in IoT networks can improve data transmission reliability and load balancing by establishing multiple paths between source and destination nodes,these networks are susceptible to security threats due to their wireless *** security solutions developed for conventional networks are often ill-suited to the unique challenges posed by IoT *** response to these challenges,this paper proposes the integration of the Ad hoc On-demand Multipath Distance Vector(AOMDV)routing protocol with a trust model to enhance network *** findings from this research demonstrate the successful fusion of AOMDV with a trust model,resulting in tangible improvements in network *** assessment of trustworthiness bolsters both security and routing capabilities in IoT *** trust model plays a crucial role in mitigating black hole attacks in IoT networks by evaluating the trustworthiness of nodes and helping in the identification and avoidance of malicious nodes that may act as black *** results validate the efficacy of the proposed trust-based routing mechanism in achieving its *** plays a pivotal role in decision-making and in the creation of secure distribution *** assessing the trustworthiness of nodes,both network security and routing efficiency can be *** effectiveness of the proposed trust-based routing mechanism is scrutinized through simulations,offering insights into its potential advantages in terms of improved network security and routing performance in the context of the IoT.

关键词： Mobile ad-hoc network(MANET) routing Ad hoc On-demand Multipath Distance Vector(AOMDV) trust model security

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SEPARATE AND DIFFUSE: USING A PRETRAINED DIFFUSION MODEL FOR BETTER SOURCE SEPARATION 12

SEPARATE AND DIFFUSE: USING A PRETRAINED DIFFUSION MODEL FOR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lutati, Shahar Nachmani, Eliya Wolf, Lior Department of Computer Science Tel Aviv University Israel School of Electrical Engineering Tel Aviv University Israel

The problem of speech separation, also known as the cocktail party problem, refers to the task of isolating a single speech signal from a mixture of speech signals. Previous work on source separation derived an upper bound for the source separation task in the domain of human speech. This bound is derived for deterministic models. Recent advancements in generative models challenge this bound. We show how the upper bound can be generalized to the case of random generative models. Applying a diffusion model Vocoder that was pretrained to model single-speaker voices on the output of a deterministic separation model leads to state-of-the-art separation results. It is shown that this requires one to combine the output of the separation model with that of the diffusion model. In our method, a linear combination is performed, in the frequency domain, using weights that are inferred by a learned model. We show state-of-the-art results on 2, 3, 5, 10, and 20 speakers on multiple benchmarks. In particular, for two speakers, our method is able to surpass what was previously considered the upper performance bound. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Source separation

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Using Layer-wise Depth Recursive Prediction to Predict Underwater Sea Temperature

Using Layer-wise Depth Recursive Prediction to Predict Under...

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2024 International Automatic Control Conference, CACS 2024

作者： Liao, Yan-Jhen Zhan, Cheng-Han Chang, Yue-Shan National Taipei University Department of Computer Science and Information Engineering Sanxia Dist New Taipei City Taiwan

ISBN: (纸本)9798350354904

The impact of sea temperature (ST) variations on climate and ecosystems is profound, posing not only significant economic losses but also potential threats to life safety. Consequently, accurate ST prediction has become an extremely important research topic. Early ocean observation data were mostly limited to the sea surface, but with the advancement of the Argo program, it is now possible to obtain ST data at various depths. Many studies have focused on predicting ST, commonly employing machine learning and deep learning techniques. However, most of these studies use only surface parameters to predict ST at different depths, leading to a significant decrease in prediction accuracy as depth increases. To address this issue, this paper proposes a method called Layer-wise Depth Recursive Prediction (LDRP). This method first trains a model to predict the ST at the uppermost depth and then uses the prediction results from the upper depth as input for the model at the next depth level, thereby improving the prediction accuracy at subsequent depths. Using data provided by the Argo program, this study combines the LDRP method with a Transformer-based model to train and predict ST at different depths. The experimental results show that the predictions made using the LDRP method exhibit improved accuracy across all depth levels compared to predictions made without LDRP, and the prediction stability at different depths is also significantly enhanced. Overall, this research not only effectively improves the accuracy of ST predictions at various depths but also mitigates the decline in prediction accuracy that occurs with increasing depth in current methods. These results demonstrate the significant potential of the LDRP method for deep ST prediction, and future studies may consider applying this method to even deeper sea temperature predictions to further verify its stability and accuracy. © 2024 IEEE.

关键词： Prediction models

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Data Analytic for Healthcare Cyber Physical System

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IEEE Transactions on Network science and engineering 2023年第5期10卷 2490-2502页

作者： Gan, Wensheng Hu, Kaixia Huang, Gengsen Chien, Wei-Che Chao, Han-Chieh Meng, Weizhi Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan National Dong Hwa University Department of Electrical Engineering Hualien97401 Taiwan UCSI University Institute of Computer Science and Innovation Kuala Lumpur Malaysia Department of Applied Mathematics and Computer Science Cyber Security Section Lyngby2800 Denmark

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data. © 2013 IEEE.

关键词： Data mining

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Fostering Metacognitive Skills in Programming: Leveraging AI to Reflect on Code 2

Fostering Metacognitive Skills in Programming: Leveraging AI...

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2nd International Workshop on Artificial INtelligent Systems in Education, AIxEDU 2024

作者： Paludo, Giulia Montresor, Alberto Department of Computer Science and Information Engineering University of Trento Via Sommarive 9 TN Trento38121 Italy

The increasing use of AI among students has significant implications for established practices across all disciplines. In the specific case of programming in computer science (CS) education, we are observing a debate between the education system which sees AI-generated code as a threat to the learning's quality, and the industry, which expects professionals to best take advantage of AI-assisted programming. In this context, a successful mediation lies in fostering skills such as metacognition and reflective learning to bridge the academic and professional worlds. This paper reviews the literature on AI-assisted practices supporting metacognition and reflective learning. Drawing on this review and the findings from a prior pilot study run by us, we designed the Reflective AI Programming Lab (RAP Lab) where groups of three students collaborate to solve programming tasks using exclusively AI-generated code, with restricted queries and a set of constraints on a designated platform which logs all the interactions between students and the AI. The approach leverages AI-driven feedback and collaboration enhancing dialogical practices as Pair Programming and promoting the development of critical reflection on AI tools in CS. By having students explain in detail their reasoning and structure their solution strategies to a third party (AI), this intervention stimulates metacognition and reflective learning by offering a different perspective on problem solving. In fact, this approach promotes a deeper comprehension of the problem and forces students to clarify and refine their thoughts when articulating their solution strategy. AI serves as an impartial non-judgmental observer, allowing students to explore their mistakes without fear of embarrassment, encouraging a risk-free environment where they are more likely to experiment, learn from their errors, and engage in deeper reflective learning. Although this approach has yet to be validated, it will serve as the basis for more

关键词： Students

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Emotion Analysis of English-Translated Tamil Literature 15

Emotion Analysis of English-Translated Tamil Literature

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swathi, K. Kamalam, G.K. Suganya Baby, N. Aadhishri, A. Kawethaa Shree, D.K. Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Kongu Engineering College Department of Information Technology Tamil Nadu India Kongu Engineering College Department of Mathematics Tamil Nadu India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu India Ssm College of Engineering Department of Management Studies Tamil Nadu India

ISBN: (纸本)9798350370249

Focus of this study is to investigate the nuanced world of emotions in English-translated Tamil literature through the lens of sentiment analysis. The dataset consists of poems expressing a range of emotions including joy, sadness, love, anger, pride, courage, and fear. To effectively analyze and classify these emotions, we utilized cutting-edge transformer-based models, like BERT, DistilBERT, and RoBERTa, to conduct our analysis. The outcomes of our study showcase the exceptional capabilities of these models in effectively analyzing emotions within poetic expressions. RoBERTa, in particular, emerged as a standout choice, consistently achieving high accuracy, precision, recall, and F1 scores across all emotion categories. Its extensive pre-training and dynamic masking techniques allowed itto excel in capturing the intricate emotional nuancesembedded in the translated poems. DistilBERT demonstrated commendable efficiency without compromising accuracy, making it a practical option for real-time sentiment analysis. BERT, although a strong performer, showcased marginally reduced efficiency compared to RoBERTa and DistilBERT. © 2024 IEEE.

关键词： BERT DistilBERT Emotion Analysis Natural Language Processing poem RoBERTa

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A Hybrid Neural Network-based Approach for Forecasting Water Demand

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computers, Materials & Continua 2022年第10期73卷 1365-1383页

作者： Al-Batool Al-Ghamdi Souad Kamel Mashael Khayyat Department of Computer Science and Artificial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Computer and Network Engineering College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Information Systems and Technology College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia

Water is a vital *** supports a multitude of industries,civilizations,and ***,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is ***,it is crucial to forecast water demand to provide it to sectors either on regular or emergency *** study aims to develop an accurate model to forecast daily water demand under the impact of climatic *** forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the *** on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’*** on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series ***,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.

关键词： Water demand forecasting artificial neural network multivariate time series climatic conditions particle swarm optimization hybrid algorithm

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Design of Battery Charger with USB Type-C Power Delivery Interface 11

Design of Battery Charger with USB Type-C Power Delivery Int...

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Trung Tran, Thanh Nhat Wang, Jian-Min Chuang, Ching-Chun Wu, Sen-Tung National Formosa University Department of Power Mechanical Engineering Taiwan National Formosa University Department of Vehicle Engineering Taiwan National Formosa University Department of Computer Science and Information Engineering Taiwan National Formosa University Department of Electrical Engineering Taiwan

ISBN: (纸本)9788957083505

This paper proposes a new solution for USB Type-C power delivery (PD) chargers. This solution mainly adopts the integrated circuit (IC) AT6201Z designed by ATK. This IC has a multi-mode control strategy, which uses the Pseudo-Digital Loop (PDL) control method to change the operation mode of the converter according to the magnitude of the inductor current. PDL control method ensures loop stability and prevents system malfunctions. To prove the feasibility of the proposed solution, a prototype charger with a maximum switching frequency of 130kHz, output power of 100W, and input voltage range of 90-264Vac/50-60Hz, is designed and implemented. Based on the experimental results, the prototype has a maximum efficiency of 92%. © 2023 The Korean Institute of Power Electronics.

关键词： Charging (batteries)

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